Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning
Abstract
A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 09, 2023
- Source ID
- 10.1073/pnas.2309240120
Entities
People
- Carolin B. Wahl
- Chad Mirkin
- Daniel W. Apley
- Hengrui Zhang
- Jie Chen
- Vinayak P. Dravid
- Wei Chen
- Wei Liu
Organizations
- Air Force Office of Scientific Research
- Division of Electrical, Communications & Cyber Systems
- Division of Materials Research
- Northwestern University
- Sherman Fairchild Foundation
- W. M. Keck Foundation